Colloquium Talk
Hung Tong, The University of Alabama
Friday November 10, 2023 at 4PM
Rocky  312

Title: Robust and Flexible Model-Based Clustering for Incomplete Data

Abstract: Model-based clustering is an important class of cluster analysis that aims to produce smaller groups of similar observations in a data set by means of finite mixture models. Since real data often contain outliers, exhibit skewness, and come in the form of partially observed records, developing robust and flexible model-based clustering methods for incomplete data has been a particularly active research area in recent years. Herein, we introduce the contaminated normal mixture for robust clustering as well as our newly developed framework for fitting it to data sets with values missing at random. Regarding flexible clustering, we discuss our work in extending special and limiting cases of the generalized hyperbolic mixture to both complete and incomplete data sets. All these mixture models are implemented and available in the R package MixtureMissing, offering a wide range of model-based clustering options to address various data challenges of researchers.